feat(prediction): vessel_analysis_results 에 분석 시점 lat/lon 저장

AnalysisResult 에 lat/lon 필드 + to_db_tuple 반영 + upsert_results SQL
컬럼 추가. 분류 파이프라인(last_row) / 경량 분석(all_positions) 두 경로
모두 분석 시점의 선박 위치를 함께 기록해 프론트 미니맵에서 특이운항
판별 위치를 실제 항적 위에 표시할 수 있게 한다.

배포 후 첫 사이클 8173/8173 lat/lon non-null 확인.

prediction/db/kcgdb.py

@@ -72,7 +72,7 @@ def upsert_results(results: list['AnalysisResult']) -> int:
     insert_sql = """
         INSERT INTO vessel_analysis_results (
             mmsi, analyzed_at, vessel_type, confidence, fishing_pct,
-            cluster_id, season, zone_code, dist_to_baseline_nm, activity_state,
+            cluster_id, season, lat, lon, zone_code, dist_to_baseline_nm, activity_state,
             ucaf_score, ucft_score, is_dark, gap_duration_min,
             spoofing_score, bd09_offset_m, speed_jump_count,
             fleet_cluster_id, fleet_is_leader, fleet_role,
@@ -88,6 +88,8 @@ def upsert_results(results: list['AnalysisResult']) -> int:
             fishing_pct = EXCLUDED.fishing_pct,
             cluster_id = EXCLUDED.cluster_id,
             season = EXCLUDED.season,
+            lat = EXCLUDED.lat,
+            lon = EXCLUDED.lon,
             zone_code = EXCLUDED.zone_code,
             dist_to_baseline_nm = EXCLUDED.dist_to_baseline_nm,
             activity_state = EXCLUDED.activity_state,

prediction/models/result.py

@@ -20,6 +20,8 @@ class AnalysisResult:
     # ALGO 01: 위치
     zone: str = 'EEZ_OR_BEYOND'
     dist_to_baseline_nm: float = 999.0
+    lat: Optional[float] = None
+    lon: Optional[float] = None
 
     # ALGO 02: 활동 상태
     activity_state: str = 'UNKNOWN'
@@ -95,6 +97,10 @@ class AnalysisResult:
 
         safe_features = _sanitize(self.features) if self.features else {}
 
+        def _opt_f(v: object) -> Optional[float]:
+            """Optional float (None 유지)."""
+            return float(v) if v is not None else None
+
         return (
             str(self.mmsi),
             self.analyzed_at,           # analyzed_at (PK 파티션키)
@@ -103,6 +109,8 @@ class AnalysisResult:
             _f(self.fishing_pct),
             _i(self.cluster_id),
             str(self.season),
+            _opt_f(self.lat),
+            _opt_f(self.lon),
             str(self.zone),             # → zone_code
             _f(self.dist_to_baseline_nm),
             str(self.activity_state),

prediction/scheduler.py

@@ -466,6 +466,9 @@ def run_analysis_cycle():
                 'registered_fishery_code': registered_fishery_code or '',
             }
 
+            # 분석 시점의 선박 위치 — 특이운항 판별 근거 좌표로 DB에 저장
+            lat_val = last_row.get('lat')
+            lon_val = last_row.get('lon')
             results.append(AnalysisResult(
                 mmsi=mmsi,
                 timestamp=ts,
@@ -474,6 +477,8 @@ def run_analysis_cycle():
                 fishing_pct=c['fishing_pct'],
                 cluster_id=fleet_info.get('cluster_id', -1),
                 season=c['season'],
+                lat=float(lat_val) if lat_val is not None else None,
+                lon=float(lon_val) if lon_val is not None else None,
                 zone=zone_info.get('zone', 'EEZ_OR_BEYOND'),
                 dist_to_baseline_nm=zone_info.get('dist_from_baseline_nm', 999.0),
                 activity_state=activity,
@@ -615,6 +620,8 @@ def run_analysis_cycle():
                     vessel_type='UNKNOWN',
                     confidence=0.0,
                     fishing_pct=0.0,
+                    lat=float(lat) if lat is not None else None,
+                    lon=float(lon) if lon is not None else None,
                     zone=zone_info.get('zone', 'EEZ_OR_BEYOND'),
                     dist_to_baseline_nm=zone_info.get('dist_from_baseline_nm', 999.0),
                     activity_state=state,